WO2003105053A1 - Markdown management - Google Patents

Markdown management Download PDF

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Publication number
WO2003105053A1
WO2003105053A1 PCT/US2003/017803 US0317803W WO03105053A1 WO 2003105053 A1 WO2003105053 A1 WO 2003105053A1 US 0317803 W US0317803 W US 0317803W WO 03105053 A1 WO03105053 A1 WO 03105053A1
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WO
WIPO (PCT)
Prior art keywords
item
gross margin
price
markdown
sales
Prior art date
Application number
PCT/US2003/017803
Other languages
English (en)
French (fr)
Inventor
Jonathan W. Woo
Original Assignee
Profitlogic, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Profitlogic, Inc. filed Critical Profitlogic, Inc.
Priority to AU2003237423A priority Critical patent/AU2003237423A1/en
Priority to CA002488197A priority patent/CA2488197A1/en
Priority to EP03736876A priority patent/EP1532558A4/en
Priority to JP2004512050A priority patent/JP2005529412A/ja
Publication of WO2003105053A1 publication Critical patent/WO2003105053A1/en

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination

Definitions

  • This description relates to markdown management.
  • a merchandiser who is planning to sell an item of fashion 5 merchandise that has a short-life-cycle typically orders an initial inventory of the item at the beginning of a season, sets an initial retail price, and offers the item to customers. Because a fashion item will have little value after the season in which it is offered, the merchandiser watches 10 the inventory level carefully. If the merchandiser believes that sales are not brisk enough to assure that the full inventory will be sold by the end of the season at the initial full price, he will reduce the price one or more times during the season with the goal of increasing the demand in order to clear out the inventory. 15 Decisions about such markdown prices (called markdown management) directly affect the retailer's profit.
  • markdown management directly affect the retailer's profit.
  • markdown management is sometimes measured by the direction and degree of change of sales and gross profit dollars from one year to the next. This approach conflates many factors 20 into one measurement, including buyer decisions, inventory allocation, promotional campaigns, sales force performance, clearance pricing decisions, macroeconomic factors, and the weather.
  • the invention features a computer-based method that includes (a) estimating price elasticity of sales of an item of commerce, (b) based on the price elasticity, determining a maximum possible gross margin for the item, and (c) using the maximum possible gross margin in connection with setting or evaluating markdown scenarios for the item.
  • Implementations of 5 the invention include one or more of the following features.
  • the item of commerce comprises an item having a product life cycle no longer than one year.
  • An optimal price schedule is generated for the item based on the maximum possible gross margin.
  • Using the maximum possible gross margin includes analyzing proposed
  • the maximum possible gross margin includes comparing the maximum possible gross margin with gross margins that result from different markdown scenarios as a basis for comparison of
  • the price elasticity comprises a separable multiplicative function of a non-time dependent elasticity term and a time-dependent base demand term.
  • the invention features a computer- based method that includes (a) for each item of a group of items of 20 commerce, determining a maximum possible gross margin, and (b) evaluating the merit of a markdown scenario for each of the items by comparing a gross margin that is based on the markdown scenario against the maximum gross margin.
  • the invention features a computer- 25 based method that includes (a) using historical sales data, expressing a consumer demand for an item of commerce as a product of two factors, one of the factors expressing a non-time dependent price elasticity of the demand for the item, the other factor expressing a composite of time-dependent demand effects, and (b) determining an optimal gross margin of the item of commerce based on the price elasticity factor.
  • the invention features a method that includes (a) with respect to a week of a selling season of an item of commerce, determining a selling price by fitting a simulation model to historical in-season data about prior sales of the item of commerce, (b) deriving unit sales for the week using a relationship
  • the markdown scenarios that are generated maybe used to evaluate the success of markdown management against an objective measure, to evaluate new analytical models, and to answer business questions (e.g.,
  • Figure 1 is a flow chart.
  • Figure 4 is a price elasticity chart.
  • Figure 5 is a histogram of gross margin percentages.
  • Figure 6 is a flow chart.
  • Figure 7 is a block diagram.
  • markdown management One goal in measuring the success of markdown management is to define a consistent method to compare the opportunities for improved margin across different items.
  • the performance of markdown management is often measured by considering the total gross margin dollars for a group of items generated by one
  • the optimal pricing schedule (e.g., markdown scenario) for the item is generated 24 by searching for an optimal gross margin for the item 26.
  • 15 historical or proposed in-season pricing schedules 28 can be determined using the price elasticity estimate and can be compared against the maximum opportunity available for that item 30.
  • an optimal markdown scenario we mean the timing and depth of a series of markdowns that provide the maximum (theoretical) 20 gross margin for an item by the end of the selling period (e.g., the end of a season).
  • the goal is to maximize the available gross margin opportunity by finding an optimal markdown scenario. 25 Note that, if the initial inventory decision were perfect such that the full inventory would be sold during the season at the full retail price, there would be no reason to take any markdown; in fact, every markdown scenario would hurt the total margin for the item. On the other hand, if a retailer had more inventory than could be sold at the full initial retail price, different markdown scenarios 5 would produce different gross margin results. Therefore, the absolute maximum gross margin of an item depends on the initial inventory and the full retail price, and markdown scenarios will determine how closely one can reach the maximum gross margin opportunity for scenarios that fall within the boundary conditions.
  • the gross margin function is:
  • p(t) is the pricing schedule
  • S(p, t) is the consumer demand 15 sales rate as a function of price p and time t
  • p s is the salvage price per unit of the inventory that is unsold at the end of the selling period
  • S e is the units sold between start time t- and the outdate (end of selling period) t e
  • c is the cost per unit of the item.
  • gross margin depends on the pricing schedule, and 20 when the pricing schedule is optimal, the maximum possible gross margin GM * can be achieved. Because the gross margin GM generated by each different pricing schedule for a given item can be meaningfully compared with other merchandise items only relative to the maximum gross margin opportunity GM * , we renormalize the definition of the gross margin GM as:
  • price elasticity we mean the sensitivity of the change in demand that is occasioned by a change in price. Using price- elasticity of an item, one can not only estimate the true maximum gross margin opportunity associated with each retail merchandise
  • an item's price elasticity 5 can be estimated 22 by analyzing historic sales data 20. Once the item's elasticity is determined, a postseason (after-the-fact) optimal markdown scenario can be determined 24.
  • Measuring the absolute maximum gross margin GM * of an item 26 would require perfect knowledge of consumer demand and 10 price elasticity. In the absence of perfect knowledge, we use as much information as possible to make a best estimation of the demand components and price elasticity.
  • SI(t y ) is a time dependent function that expresses the seasonality of demand (bathing suits are in higher demand in May than in September, for example).
  • PLC(t) (4) is the product life cycle function (fashion shoes have a peak of demand shortly after sales begin, and the demand trails off over time)
  • N is a normalization parameter
  • t k is a model parameter represents the peak time of the product life cycle function
  • C a constant baseline offset model parameters (define).
  • R(p) is the a price elasticity function and is defined in equation 8 below, and the inventory effect function is
  • I(t) is the inventory at time t
  • the initial inventory is I-
  • the critical inventory level I c . is a model parameter, below this
  • the inventory effect function expresses the notion that sales are adversely affected when the inventory falls below a critical level.
  • Price elasticity is a key factor in markdown management.
  • the fact 20 that demand changes in response to a markdown is a fundamental dynamic principle of markdown management. Therefore, it is important to separate the markdown effect from other components of the demand function.
  • Empirical evidence indicates that using a separable multiplicative approach has no significant flaw. Empirical evidence also indicates that using a time-independent formulation is justified. That formulation is also supported by the fact that we are focusing on short-life-cycle items.
  • the postseason optimal pricing schedule is intended to represent the best pricing schedule possible given the client's business rules and observed week-by-week sales for the item. Examples of
  • the demand model may be summarized in a single equation, which attempts to capture the effect on demand of changes in price and inventory level, relative to their historical values and 25 independent of all other factors. Relative to the observed price p and inventory level / , the new price p' affects demand through the price elasticity ⁇ , and the new inventory level I' affects demand through the inventory effect and its critical inventory level E :
  • An ultimate goal is to perform in-season simulation of markdown scenarios while the season is in progress. For each week of a simulated season, the simulation software will be applied to make a fit to historical data and determine a new price. The new price
  • Empirical results have obtained from using an actual specialty retailer's data to perform two components of data analysis and simulation: analytic model fit and post-season optimal markdown scenario measurement.
  • figure 2 shows data from one specialty retailer and figure 3 shows data from another specialty retailer. (Both retailers sell women's merchandise).
  • the overall baseline demand model is represented by solid black lines 38 and the actual sales unit data 40 are represented by blue starred lines.
  • a typical pricing schedule 42 is essentially a series of steps.
  • the most sensitive price elasticity information is embedded in the boundaries of the price steps (please define what you mean by boundaries of the price steps (e.g., significant price 20 changes week to week) ).
  • Our fitting algorithm takes advantage of this insight by weighting the effects of bigger week-to-week price changes more heavily relative to the full price more heavily. This weighting consistently causes the model to better follow the sales demand change from the markdown effect.
  • FIG. 25 A summary plot of price elasticity estimated from model fittings with historic data for 150 items is shown in figure 4. The plot shows a reasonable distribution with most items falling between price elasticity parameters (gamma) of 1.0 to 2.5.
  • Figure 5 shows the number density distribution of percent gross margin differences between post-season optimal simulation results and actual historic 5 data for 150 items normalized by the optimal results for the 150 items. This plot shows that there is room to improve gross margin of these items by 11 % on average.
  • the historical sales data may be stored on a mass- storage medium 90 for use by a server 92.
  • the server includes a microprocessor 94 controlled by system software 96 and markdown software 98 stored in memory 100.
  • the markdown software performs all of the functions described above including
PCT/US2003/017803 2002-06-07 2003-06-06 Markdown management WO2003105053A1 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
AU2003237423A AU2003237423A1 (en) 2002-06-07 2003-06-06 Markdown management
CA002488197A CA2488197A1 (en) 2002-06-07 2003-06-06 Markdown management
EP03736876A EP1532558A4 (en) 2002-06-07 2003-06-06 MARK DOWN MANAGEMENT
JP2004512050A JP2005529412A (ja) 2002-06-07 2003-06-06 値下げ運用

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/165,041 US7251615B2 (en) 2002-06-07 2002-06-07 Markdown management
US10/165,041 2002-06-07

Publications (1)

Publication Number Publication Date
WO2003105053A1 true WO2003105053A1 (en) 2003-12-18

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US (1) US7251615B2 (ja)
EP (1) EP1532558A4 (ja)
JP (1) JP2005529412A (ja)
AU (1) AU2003237423A1 (ja)
CA (1) CA2488197A1 (ja)
WO (1) WO2003105053A1 (ja)

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JP2006338585A (ja) * 2005-06-06 2006-12-14 Hitachi Ltd 需要予測方法、および需要予測分析サーバ、並びに需要予測プログラム

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